Effects of polyacrylamide‐co‐acrylic acid on cellulose production by <i>Acetobacter xylinum</i>
Bibliographic record
Abstract
Abstract Polyacrylamide‐co‐acrylic acid (PA) added to shake flask cultures of Acetobacter xylinum at concentrations up to 3 g dm−3 resulted in increased production of bacterial cellulose. For PA concentrations of 0–3 g dm−3, 7‐day cellulose production rose monotonically from 2.7 ± 0.8 to 6.5 ± 0.5 g dm−3 at a shaker speed of 175 rpm, and from 1.7 ± 0.01 to 3.7 ± 0.5 g dm−3 at shaker speed of 375 rpm. Addition of PA also changed the morphology of the biomass from amorphous/stringy forms to spheroidal particles with diameters ≤2 mm. Similarly, bioreactor cultures grown in the absence of PA formed long fibrous masses which deposited on the internals, while those grown in the presence of 1–2 g dm−3 PA formed small discrete particles with diameters ≤0.1 mm. Tests performed with 1 and 2 g dm−3 PA, and stirrer speeds from 500 to 900 rpm, appeared to give the highest cellulose concentration of 5.3 ± 0.7 g dm−3 in 64–68.5 h in the presence of 2 g dm−3 PA at 700 rpm, although this value was statistically indistinguishable from that obtained at 1 g dm−3 PA and 900 rpm. A qualitative model is proposed to describe the mechanisms by which PA affects biomass morphology, resulting in its advantageous formation as small, dispersed, spheroidal pellets. Quantitative analysis of the results gave inverse correlations between both the fraction of fructose carbon going to cellulose synthesis and the specific fructose consumption rate, and the maximum cellulose concentration and the fraction of fructose carbon going to by‐product formation. Since cellulose yield was almost universally improved by higher polyacrylamide concentration, it appears likely that increased viscosity reduces fructose uptake rate by limiting mass transfer. Copyright © 2003 Society of Chemical Industry
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".